Characterizing the Brazilian airspace structure and air traffic performance via trajectory data analytics

This paper presents a data-driven approach for multi-scale characterization of the Brazilian airspace structure and air traffic operational performance from aircraft tracking data recorded by surveillance systems. Unsupervised learning is performed with a flight trajectory clustering analysis to automatically identify spatial traffic patterns in both the terminal and the en route airspace for major origin-destination pairs of the Brazilian air transportation system. Based on the as-flown route structure learned, quantitative metrics are developed to describe the structural efficiency of the airspace and the operational efficiency of the traffic flows. For this, actual flight trajectories are projected onto reference nominal trajectories in space and time. The results allowed for cross-route comparisons of air traffic flow efficiency across multiple flight phases as well as for the identification of causal factors for trajectory deviations from nominal routes. An interactive data analytics tool is also created to output performance statistics and air traffic visualizations. With the provision of a systematic data-driven approach for characterizing actual air traffic operations, the analytics framework is envisioned to assist airspace design and performance monitoring processes and to provide the basis for developing predictive capabilities in support of traffic flow management.     

Statistical characterization of deviations from planned flight trajectories in air traffic management

Understanding the relation between planned and realized flight trajectories and the determinants of flight deviations is of great importance in air traffic management. In this paper we perform an in-depth investigation of the statistical properties of planned and realized air traffic on the German airspace during a 28 day periods, corresponding to an AIRAC cycle. We find that realized trajectories are on average shorter than planned ones and this effect is stronger during night-time than day-time. Flights are more frequently deviated close to the departure airport and at a relatively large angle-to-destination. Moreover, the probability of a deviation is higher in low traffic phases. All these evidences indicate that deviations are mostly used by controllers to give directs to flights when traffic conditions allow it. Finally we introduce a new metric, termed di-fork, which is able to characterize navigation points according to the likelihood that a deviation occurs there. Di-fork allows to identify in a statistically rigorous way navigation point pairs where deviations are more (less) frequent than expected under a null hypothesis of randomness that takes into account the heterogeneity of the navigation points. Such pairs can therefore be seen as sources of flexibility (stability) of controllers traffic management while conjugating safety and efficiency.     

Analyzing tactical control strategies for aircraft arrivals at an airport using a queuing model

This paper proposes to analyze control strategies for arrival air traffic at an airport using a classical queuing model. The parameters of our model are estimated by means of a data-driven analysis of two years of radar tracks and flight plans for arrival flights at Tokyo International Airport from 2016 to 2017. Our results show that increasing the capacity with one or two more aircraft in the airspace up to 60 NM around the airport significantly mitigates arrival delays, even when assuming future, increased arrival traffic volumes. The outcomes of this study provide insights into the effectiveness of arrival control strategies and are seen as a means to recommend scenarios to be further analyzed with human-in-the-loop simulations.     

Air-traffic complexity resolution in multi-sector planning

This paper considers the problem of minimizing the traffic complexities in an airspace of adjacent sectors. The traffic complexity of a sector is determined by the numbers of flights within it, near its border, and on non-level segments within it. The dimensions of complexity resolution involve changing the take-off times of non-airborne flights, changing the approach times into the chosen airspace of airborne flights by slowing and accelerating within the two layers of feeder sectors around that airspace, as well as changing the altitude at way-points in that airspace. Experiments with European flight profiles from the Central Flow Management Unit show that these forms of resolution can lead to significant complexity reductions and rebalancing.     

A causal factors analysis of aircraft incidents due to radar limitations: The Norway case study

Provision of seamless, safe and effective surveillance services to airspace users requires high performance surveillance sensor coverage in the whole airspace. Limitations in the surveillance system will lead to an inability to provide the required surveillance services to the users. This may result in aircraft incident and accident occurrences. In this paper a case study is developed for the Norwegian airspace, based on five years of safety reports, to identify causal factors of incidents/accidents due to radar system limitations. This is conducted with a safety data analysis from Avinor – Norway's Air Navigation Service Provider (ANSP) and structured communication with Surveillance/ATM safety experts from Avinor. The case study shows that, 76 out of 124 occurrences within the five years in the Norwegian airspace/airport were related to the surveillance function, and 34 out of the 76 occurrences were due to limitations in the radar systems. The analysis identified that the highest contributing causal factors of the occurrences due to radar system limitations were limited surveillance coverage, followed by the lack of situational awareness for flight crew/controllers and unsynchronised surveillance information between flight crew and controllers.     

The evaluation of Istanbul airspace design from the perspective of complexity and safety

The approach, descent, and climb phases of flights in terminal maneuvering areas (TMAs) are some of the critical parts of a flight in which more than half of accidents happen. For an ANSP (Air Navigation Service Provider), to fulfill the responsibility with a high-quality service degree while maintaining safety, these difficult areas should be designed carefully and equipped with the most efficient procedures. Point merge system (PMS), which is defined as a systemized method for sequencing arrival flows, is launched in Istanbul's new TMA (LTFM TMA) being designed by DHMI (Devlet Hava Meydanları Isletmesi) to improve safety and efficiency. In this paper, the airspace complexity is based on adjusted density and structural index, and safety indicators. To investigate the effects of the radical changes made in re-organized LTFM TMA on the safety issue in comparison with the previous terminal maneuvering area, LTBA TMA, six months' data consisting of 75215 arrival and departure flights are used. Results reflect that the LTFM TMA, one of the world's busiest terminal airspaces, has statistically significantly lower scores in terms of conflict numbers per aircraft, complexity metrics, adjusted density, the hour of interactions, and flight hours than LTBA TMA. Lastly, there exists no longer a significant relationship between conflict types and complexity after launching the new design and structure.     

Optimising flight connection times in airline bank structure through Simulated Annealing and Tabu Search algorithms

In hub and spoke airline networks, flight arrivals and departures generally have a bank structure to increase connections among spoke cities through a hub airport in order to provide cheaper service for higher volumes of air traffic. In this study, we introduce the airline bank optimisation problem with a novel mathematical model for improving flight connection times. The mathematical model aims to minimise the total connection times for transfer passengers and generates flight schedules regarding slot capacities in the hub airports. Since the problem is a combinatorial optimisation problem NP-hard and computational complexity increases rapidly for real-world problems, we employ the simulated annealing and the tabu search algorithms to achieve better solutions in a reasonable time. We generate sub-problems using real-world data and investigate the effectiveness of the algorithms. Finally, we present the results of a real case study of a Turkish airline company which has a hub airport connecting the flights between Middle Eastern and European cities.     

Cost savings from trajectory deviations in the European air space

Air transport deregulation has lead to an increase of air traffic, together with a reduction of air fares. Air fare reduction has narrowed operational margins of airlines, bringing financial and employment instability. This has brought airlines to pay increasing attention to flying costs reduction. Two important components of flying costs airlines can try to cut modifying the planned flight are en route charges and operational costs. We rely on Demand Data Repository (DDR2) data to calculate deviations from planned flight trajectories to analyse the extent to what airlines try to cut operational costs making shorter flights than planned if possible, and cut en route charges providing a planned flight with lower en route charges than the planned flight. Our findings show that there is no generalised strategy among airlines to reduce en-route charges asking for deviations of the planned route. On the other hand, airlines are achieving savings of operational costs regularly. Higher savings per nautical mile are obtained in night flights, with longer planned distance and operated by low cost carriers.     

Analyzing the effect of aviation infrastructure over aviation fuel consumption reduction

The purpose of this paper is to examine the effect of various aviation infrastructure dimensions over aviation fuel consumption reduction (AFCR) performance. This study is an effort that considers the role of dimensions collectively from all aspects belonging to aviation infrastructure. The relevance of dimensions and constructs for hypothesis development are based on extensive literature review. Exploratory factor analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed in the consecutive purification processes. Also, hypothesis testing was conducted using Structural Equation Modeling (SEM). A customized questionnaire was developed for collecting data from both kinds of respondents: Aviation industry experts and academic experts. Out of 382 approaches through mail survey, a total of 194 valid responses were collected. Analysis of the results shows the positive and significant impact of various factors such as: airport design, airspace management and air traffic control over the aviation fuel consumption reduction. Maximum importance is adjudged on air traffic control (ATC) and airspace route flexibility. The results of this study will encourage airlines and airport development authorities to increase their insight over aviation infrastructure, also to perform deeper analysis and find out precise values for real life implications.     

Impact of Continuous Climb Operations in ATC workload. Case-study Palma airport

This research assesses the impact of the integration of Continuous Climb operations (CCOs) on Air Traffic Control (ATC) workload. The methodology encompasses different modules: CCO, standard departing and arriving trajectories extracted from an external database, an ad-hoc algorithm for detecting and solving conflicts, and an ATC-workload model with the inclusion of CCO-task modifications. Monte Carlo simulations evaluates different combinations of these modules. Then, a sensitivity analysis is performed to evaluate two parameters: the impact of the calibration of the maximum ATC workload and the percentage increase of the CCO tasks on the ATC workload. The methodology is applied to a case study at Palma airport in Spain. Extensive numerical simulations are executed based on the integration of CCOs into the system from 0% to 100%. The integration of CCOs implies the increase of the ATC workload in the Control Tower (TWR) and the reduction in the Approach Control Centre (APP). The sensitivity analysis shows that the decrease in the increase of CCO-task workload barely affects the number of CCOs that can be operated without exceeding the workload limits. Conversely, the reduction of the ATC workload calibration allows the integration of CCOs around 50% in the case of 90% TWR calibration and up to 100% in the case of 80%.     

Control-based optimization approach for aircraft scheduling in a terminal area with alternative arrival routes

This paper presents an optimization approach for dynamically scheduling aircraft operations and supporting air traffic controllers in both determining and implementing operationally feasible landing and departure times at an airport. The mixed integer linear programming model proposed incorporates air traffic control infrastructure in terms of route network, introduces the concept of alternative approach routes and is designed to generate an output that can be converted into effective advisories for executable flight commands. It shows reasonable computational times for obtaining the optimal solution and delay reductions of up to 35% with practical size instances from Sao Paulo/Guarulhos International Airport.     

Flight and passenger efficiency-fairness trade-off for ATFM delay assignment

This paper studies trade-offs between efficiency (performance) and fairness (equity), when assigning ATFM delay pre-tactically (on-ground at origin airport) due to reduced airport capacity at destination. Delay is assigned as the result of the optimisation of a deterministic multi-objective problem considering flight and passenger perspectives when defining objectives of performance and fairness. Two optimisation cases are presented: one where objectives are based on flight metrics, and another one where they are based on passenger metrics. The paper defines and analyses efficiency-fairness trade-offs: the concepts of price of fairness for flights and passengers are defined as the percentage of efficiency loss due to the consideration in the optimisation of fairness; whereas the price of efficiency is considered as the fairness loss relative to the maximum value of the fairness metric, when considering flight or passenger delay in the optimisation. The optimisation model is based on the ground holding problem and uses various objective functions. For performance, total delay for flights (considering reactionary delay), and total delay for passengers (considering outbound connections) are defined. For fairness, the deviation of flight arrivals from a Ration By Schedule solution, and the deviation of delay experienced by passengers with respect to the one obtained in an RBS situation are used. An illustrative application on traffic at Paris Charles de Gaulle airport, a busy European hub airport, and including realistic values of traffic is modelled. A comprehensive trade-off analysis is presented. Results show, how in some cases, gains on one stakeholder can be achieved without implying any detriment on the other one. Passengers are more sensitive to the optimisation and hence, their consideration when assigning delay is recommended. Further research should explore how to combine flight and passenger indicators in the optimisation and consider how the lack of data availability could be mitigated.     

Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning

In recent years, convective weather has been the cause of significant delays in the European airspace. With climate experts anticipating the frequency and intensity of convective weather to increase in the future, it is necessary to find solutions that mitigate the impact of convective weather events on the airspace system. Analysis of historical air traffic and weather data will provide valuable insight on how to deal with disruptive convective events in the future. We propose a methodology for processing and integrating historic traffic and weather data to enable the use of machine learning algorithms to predict network performance during adverse weather. In this paper we develop regression and classification supervised learning algorithms to predict airspace performance characteristics such as entry count, number of flights impacted by weather regulations, and if a weather regulation is active. Examples using data from the Maastricht Upper Area Control Centre are presented with varying levels of predictive performance by the machine learning algorithms. Data sources include Demand Data Repository from EUROCONTROL and the Rapid Developing Thunderstorm product from EUMETSAT.     

Airport traffic complexity and environment efficiency metrics for evaluation of ATM measures

For a given (current or planned) traffic demand, different air traffic management measures could result in different airport traffic complexity and efficiency. This paper presents the research on the relationship between airport traffic complexity and time and environmental efficiency for different air traffic control (ATC) tactics applied to the given or planned airport layout. Emphasis is placed on the evaluation of airport traffic complexity, aircraft fuel consumption, gas emissions and time efficiency for different ATC tactics and/or airport airfield layouts. For busy airports during peak hours, arrival queuing delays, taxi-in, taxi-out times and departure queuing delays increase, which induces additional unnecessary fuel consumption, gas emission and time inefficiency. In order to find a tool which could indicate potential delay generators, a measure of airport traffic complexity – called Dynamic Complexity is proposed. Experiments were performed for airports with different airfield layouts, for different traffic demands and ATC applied tactics using SIMMOD simulation model. Traffic situations were analyzed and delays were measured. The values of airport traffic complexity, fuel consumption and gas emissions were also determined. A comparative analysis of the results show: first, the proposed airport traffic complexity metric quite satisfactorily reflects the influence of traffic characteristics upon the environmental state of the system, and second, different ATM strategic and tactical measures (airport airfield infrastructure development and applied ATC tactics) could significantly reduce traffic complexity and increase time and environmental efficiency at the airport.     

Handling the pseudo pilot assignment problem in air traffic control training by using NASA TLX

Simulator courses play an important role in a department training air traffic controllers (ATCOs). One of the most important elements of these courses is a pseudo-pilot (PP) who has active tasks during simulator training. At Eskisehir Technical University (ESTU) PP assignments are made manually to meet the demand of related courses by considering the availability of PP. Even where it is attempted to assign each PP equally in terms of period, personal workloads differ due to the different psychological (mental) and physiological requirements of the simulator tasks. In this study, the PP assignment problem is investigated using mixed-integer programming (MIP). For this purpose, firstly, an equal period assignment to pilots was attempted with a mathematical model, called the Equality of Periods Model (EPM). Then, simulator tasks were weighted using the NASA Task Load Index (NASA TLX), and an Equality of Workload Model (EWLM) was created based on these weights. Finally, these models were combined to make fair assignments with the Fair Model (FM). The results indicate that the proposed models significantly reduce the differences of workload and working period compared to manual assignment (MA).     

A method to estimate air traffic controller mental workload based on traffic clearances

Workload estimation is a complex domain which has been investigated extensively over the years. Past estimation techniques have focused on measuring workload directly from the air traffic controllers (ATCOs) or inferring it from traffic factors. The limitations of these techniques are interfering into the ATCO job and not being able to capture the differences amongst individual ATCOs respectively. This paper presents a novel technique overcoming these limitations, able to accurately estimate the workload experienced by the ATCO based exclusively on the clearances provided to air traffic. The technique, which was calibrated for the EUROCONTROL Maastricht Upper Area Control (MUAC) Centre, thereby has the potential to more accurately estimate actual airspace capacity. It is independent of the level of system automation and therefore applicable not only with the current ATM system, but also in the anticipated future highly automated environments as well as during the transition period. The paper discusses potential applications such as real time monitoring of operational workload and post-operations identification of sector workload imbalances. Both can contribute towards enhancing the performance of the ATM system.     

Managing severe airspace flow programs: The Airlines’ side of the problem

This paper presents a heuristic-based approach for minimizing airlines’ schedule disruptions and operation costs associated with severe airspace flow programs. It considers primary decisions made by flight dispatchers such as flight slot substitution and rerouting outside the boundaries of the flow-constrained area. A two-stage heuristic is developed. In the first, a linear approximation of the problem is used to screen inefficient routing and slot substitution alternatives. The second stage examines possible solution improvements through trading flight assignments for every pair of conflicting routes. A genetic algorithm is developed and used to benchmark the performance of the two-stage heuristic. In the algorithm, flight route and slot allocation schemes are modeled as chromosomes. The fitness of these chromosomes measures the magnitude of schedule disruption and overall operating cost. A set of experiments that compare the performance of the two heuristics considering airspace flow programs with different levels of severity is presented.     

Mitigation of airspace congestion impact on airline networks

In recent years European airspace has become increasingly congested and airlines can now observe that en-route capacity constraints are the fastest growing source of flight delays. In 2010 this source of delay accounted for 19% of all flight delays in Europe and has been increasing with an average yearly rate of 17% from 2005 to 2010. This paper suggests and evaluates an approach to how disruption management can be combined with flight planning in order to create more proactive handling of the kind of disruptions, which are caused by congested airspace. The approach is evaluated using data from a medium size European carrier and estimates a lower bound saving of several million USD.     

Using causal machine learning for predicting the risk of flight delays in air transportation

Delays in air transportation are a major concern that has negative impacts on the airline industry and the economy. Given the complexity of the National Air Space system, predicting the risk of flight delays and identifying significant predictors is vital to risk mitigation. The purpose of this paper is to perform data mining using causal machine learning algorithms in the USELEI process (Understanding, Sampling, Exploring, Learning, Evaluating, and Inferring) to predict the probability of flight delays in air transportation using data collected from different sources. The findings indicated significant effects of predictors, including reported arrivals and departures, arrival and departure demands, capacity, efficiency, and traffic volume at the origin and destination airports on the risk of flight delays. More importantly, causal interrelationships among variables in a fully structural network are presented to how these predictors interact with one another and how these interactions lead to delay incidents. Finally, sensitivity analysis and causal inference can be performed to evaluate various what-if scenarios and form effective strategies to mitigate the risk of delays.     

Safety perceptions of training pilots based on training institution and experience

This study examines training pilot survey data in order to determine how students’ years of education and the institutions that they attend affect their perceptions of the risk factors in aviation as assessed using the SHELL model (software, hardware, environment, and liveware). The results reveal that student pilots lack confidence with respect to their knowledge during flights; moreover, they fail to recognize the importance of maintaining relationships among supporting staff such as air traffic controllers, mechanics, and others involved in the flight process. The findings suggest that to meet an increased demand for pilots, newly approved training centers are needed, centers which will foster awareness of interaction between human factors and other aspect of aviation safety; to support this, there should be more standardization of curricula.